{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging and Pasting\n",
    "Decision Tree ensemble is trained on feature set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 0.10924883,  1.83030605, -0.14807631, ...\n",
      "X_32_val                  -> array([[ 0.66944195,  0.46536115,  0.79919788, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32train_std             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[ -6.40490\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_train                   -> array([[[ 0.00119031,  0.00873315,  0.00641749, ..\n",
      "X_train_std               -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([6, 6, 5, ..., 0, 4, 1])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 4, ..\n",
      "y_32_train                -> array([0, 3, 4, ..., 0, 3, 1])\n",
      "y_32_val                  -> array([2, 2, 4, ..., 0, 7, 3])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([6, 6, 5, ..\n",
      "y_train                   -> array([0, 3, 4, ..., 0, 3, 1])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 16) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 16) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_train_std.shape, \"and labels: \", y_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_test_std), X_test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging: training took 7.99 seconds\n",
      "BaggingClassifier:\n",
      "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best'),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=100, n_estimators=400, n_jobs=1, oob_score=False,\n",
      "         random_state=None, verbose=0, warm_start=False)\n",
      " \n",
      "Pasting: training took 7.47 seconds\n",
      "PastingClassifier:\n",
      "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best'),\n",
      "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=100, n_estimators=400, n_jobs=1, oob_score=False,\n",
      "         random_state=None, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "bagg_clf = BaggingClassifier(DecisionTreeClassifier(), bootstrap=True, n_estimators=400, max_samples=100)\n",
    "paste_clf = BaggingClassifier(DecisionTreeClassifier(), bootstrap=False, n_estimators=400, max_samples=100)\n",
    "\n",
    "start = time()\n",
    "bagg_clf.fit(X_train_std, y_train)  \n",
    "print(\"Bagging: training took %.2f seconds\"%(time() - start))\n",
    "print(\"BaggingClassifier:\")\n",
    "print(bagg_clf)\n",
    "\n",
    "print(\" \")\n",
    "\n",
    "start = time()\n",
    "paste_clf.fit(X_train_std, y_train)  \n",
    "print(\"Pasting: training took %.2f seconds\"%(time() - start))\n",
    "print(\"PastingClassifier:\")\n",
    "print(paste_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test bagging classifier\n",
      " \n",
      "Accuracy on -20 dB SNR samples =  0.12375\n",
      "Accuracy on -18 dB SNR samples =  0.1255\n",
      "Accuracy on -16 dB SNR samples =  0.13725\n",
      "Accuracy on -14 dB SNR samples =  0.13825\n",
      "Accuracy on -12 dB SNR samples =  0.15975\n",
      "Accuracy on -10 dB SNR samples =  0.1815\n",
      "Accuracy on -8 dB SNR samples =  0.242\n",
      "Accuracy on -6 dB SNR samples =  0.30425\n",
      "Accuracy on -4 dB SNR samples =  0.35025\n",
      "Accuracy on -2 dB SNR samples =  0.39475\n",
      "Accuracy on 0 dB SNR samples =  0.44425\n",
      "Accuracy on 2 dB SNR samples =  0.562\n",
      "Accuracy on 4 dB SNR samples =  0.7415\n",
      "Accuracy on 6 dB SNR samples =  0.7735\n",
      "Accuracy on 8 dB SNR samples =  0.785\n",
      "Accuracy on 10 dB SNR samples =  0.783\n",
      "Accuracy on 12 dB SNR samples =  0.79125\n",
      "Accuracy on 14 dB SNR samples =  0.7925\n",
      "Accuracy on 16 dB SNR samples =  0.7865\n",
      "Accuracy on 18 dB SNR samples =  0.78675\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy_bagg = defaultdict(list)\n",
    "\n",
    "print(\"Test bagging classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = bagg_clf.predict(X_test_std[snr])\n",
    "    accuracy_bagg[snr] = metrics.accuracy_score(y_test[snr], y_pred[snr])\n",
    "    print(\"Accuracy on %d dB SNR samples = \"%(snr), accuracy_bagg[snr])   \n",
    "    \n",
    "accuracy_bagg = collections.OrderedDict(sorted(accuracy_bagg.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test pasting classifier\n",
      " \n",
      "Accuracy on -20 dB SNR samples =  0.13\n",
      "Accuracy on -18 dB SNR samples =  0.12925\n",
      "Accuracy on -16 dB SNR samples =  0.1315\n",
      "Accuracy on -14 dB SNR samples =  0.12325\n",
      "Accuracy on -12 dB SNR samples =  0.15175\n",
      "Accuracy on -10 dB SNR samples =  0.18475\n",
      "Accuracy on -8 dB SNR samples =  0.2595\n",
      "Accuracy on -6 dB SNR samples =  0.30775\n",
      "Accuracy on -4 dB SNR samples =  0.34775\n",
      "Accuracy on -2 dB SNR samples =  0.3845\n",
      "Accuracy on 0 dB SNR samples =  0.4425\n",
      "Accuracy on 2 dB SNR samples =  0.56875\n",
      "Accuracy on 4 dB SNR samples =  0.7475\n",
      "Accuracy on 6 dB SNR samples =  0.7795\n",
      "Accuracy on 8 dB SNR samples =  0.78175\n",
      "Accuracy on 10 dB SNR samples =  0.79025\n",
      "Accuracy on 12 dB SNR samples =  0.7835\n",
      "Accuracy on 14 dB SNR samples =  0.79525\n",
      "Accuracy on 16 dB SNR samples =  0.79025\n",
      "Accuracy on 18 dB SNR samples =  0.79125\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy_paste = defaultdict(list)\n",
    "\n",
    "print(\"Test pasting classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = paste_clf.predict(X_test_std[snr])\n",
    "    accuracy_paste[snr] = metrics.accuracy_score(y_test[snr], y_pred[snr])\n",
    "    print(\"Accuracy on %d dB SNR samples = \"%(snr), accuracy_paste[snr])   \n",
    "    \n",
    "accuracy_paste = collections.OrderedDict(sorted(accuracy_paste.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f00e81c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArkAAAGcCAYAAADd17isAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwyKXiIiIiDzO/wEwl7K96/pUQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f00e989b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArkAAAGcCAYAAADd17isAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f011dea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the performance \n",
    "\n",
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy_bagg.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('Bagging : SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n",
    "#----------------------------------------------------------\n",
    "\n",
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy_paste.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('Pasting : SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
